Natural Language Processing (NLP)

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Foundation of Natural Language Processing

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• Overview computational linguistic.
• History of NLP
• Why NLP
• Use of NLP
• Language modeling with N-gram
• Spelling correction
• Neural networks and neural language models
• Parts-of-Speech tagging
• Syntactic parsing
• Language semantics
• Computational semantics

Text Analytics, Processing, and Predictive Modeling

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• Introduction to text analytics (text encoding, regular expressions*, word frequencies & stop words, tokenization, bag-of-words representation, stemming & lemmatization, TF-IDF)
• The Naive Bayes algorithm (Bayes’ theorem and its building blocks, Naive Bayes for text classification)

Text Processing Importing text.

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• Web scrapping.
• Text processing
• Understanding regex.
• Text normalization
• Word count.
• Frequency distribution.
• Text annotation.
• Use of annotator.
• String tokenization
• Annotator creation.
• Sentence processing.
• Lemmatization in text processing
• POS.
• Named entity recognition
• Dependency parsing in text.
• Sentimental analysis

Word embedding

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• Word embedding
• Co-occurrence vectors
• Word2vec
• Doc2vec

RNN for NLP

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• Recurrent neural networks.
• Long short term memory (LSTM)
• Bi LSTM.
• Stacked LSTM
• GRU implementation.
• Building a story writer using character level RNN.

Attention based model

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• Seq2Seq.
• Encoders and decoders.
• Attention mechanism.
• Attention neural networks
• Self-attention

Transfer learning in NLP

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• Introduction to transformers.
• Bert model.
• Elmo model.
• GPT2 model
• GPT3 model.
• Albert model.
• Distilbert model

Transformers for NLP

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• GPT3
• BERT

NLP Libraries

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Spacy

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• Spacy overview
• Spacy function
• Spacy function implementation in text processing.
• Pos tagging, challenges and accuracy.
• Entities and named entry recognition
• Interpolation, language models
• NLTK
• Text blob
• Stanford NLP